Exploitation Of Vulnerabilities Research Articles

The CMG helicase and cancer: a tumor "engine" and weakness with missing mutations.

The replicative Cdc45-MCM-GINS (CMG) helicase is a large protein complex that functions in the DNA melting and unwinding steps as a component of replisomes during DNA replication in mammalian cells. Although the CMG performs this important role in cell growth, the CMG is not a simple bystander in cell cycle events. Components of the CMG, specifically the MCM precursors, are also involved in maintaining genomic stability by regulating DNA replication fork speeds, facilitating recovery from replicative stresses, and preventing consequential DNA damage. Given these important functions, MCM/CMG complexes are highly regulated by growth factors such as TGF-ß1 and by signaling factors such as Myc, Cyclin E, and the retinoblastoma protein. Mismanagement of MCM/CMG complexes when these signaling mediators are deregulated, and in the absence of the tumor suppressor protein p53, leads to increased genomic instability and is a contributor to tumorigenic transformation and tumor heterogeneity. The goal of this review is to provide insight into the mechanisms and dynamics by which the CMG is regulated during its assembly and activation in mammalian genomes, and how errors in CMG regulation due to oncogenic changes promote tumorigenesis. Finally, and most importantly, we highlight the emerging understanding of the CMG helicase as an exploitable vulnerability and novel target for therapeutic intervention in cancer.

Analyzing Malicious Activities and Detecting Adversarial Behavior in Cryptocurrency based Permissionless Blockchains: An Ethereum Usecase

Different malicious activities occur in cryptocurrency-based permissionless blockchains such as Ethereum and Bitcoin. Some activities are due to the exploitation of vulnerabilities which are present in the blockchain infrastructure, some activities target its users through social engineering techniques, while some activities use it to facilitate different malicious activities. Since cryptocurrency-based permissionless blockchains provide pseudonymity to its users, bad actors prefer to carry out transactions related to malicious activities on them. Towards this, we aim at automatically flagging blockchain accounts as suspects that indulge in malicious activities, thus reducing the unintended support that cryptocurrency-based permissionless blockchains provide to malicious actors. We first use the cosine similarity (CS) metrics to study the similarities between the feature vector of accounts associated with different malicious activities and find that most of the malicious activities associated with the Ethereum blockchain behave similarly. We then use the K-Means clustering algorithm to check if accounts associated with similar malicious activities cluster together. We also study the effect of bias on the performance of a machine learning (ML) algorithm, due to the number of accounts associated with malicious activity. We then compare the different state-of-the-art models and identify that Neural Networks (NNs) are resistant to bias associated with a malicious activity and are also robust against adversarial attacks. The previously used ML algorithms for identifying malicious accounts also show bias towards an over-represented malicious activity.

GATOR2-dependent mTORC1 activity is a therapeutic vulnerability in FOXO1 fusion-positive rhabdomyosarcoma.

Oncogenic FOXO1 gene fusions drive a subset of rhabdomyosarcoma (RMS) with poor survival; to date, these cancer drivers are therapeutically intractable. To identify new therapies for this disease, we undertook an isogenic CRISPR-interference screen to define PAX3-FOXO1-specific genetic dependencies and identified genes in the GATOR2 complex. GATOR2 loss in RMS abrogated aa-induced lysosomal localization of mTORC1 and consequent downstream signaling, slowing G1-S cell cycle transition. In vivo suppression of GATOR2 impaired the growth of tumor xenografts and favored the outgrowth of cells lacking PAX3-FOXO1. Loss of a subset of GATOR2 members can be compensated by direct genetic activation of mTORC1. RAS mutations are also sufficient to decouple mTORC1 activation from GATOR2, and indeed, fusion-negative RMS harboring such mutations exhibit aa-independent mTORC1 activity. A bisteric, mTORC1-selective small molecule induced tumor regressions in fusion-positive patient-derived tumor xenografts. These findings highlight a vulnerability in FOXO1 fusion-positive RMS and provide rationale for the clinical evaluation of bisteric mTORC1 inhibitors, currently in phase I testing, to treat this disease. Isogenic genetic screens can, thus, identify potentially exploitable vulnerabilities in fusion-driven pediatric cancers that otherwise remain mostly undruggable.

Erratum to: On the Detection of Exploitation of Vulnerabilities That Leads to the Execution of a Malicious Code

Erratum to: On the Detection of Exploitation of Vulnerabilities That Leads to the Execution of a Malicious Code

Analysis and implementation of semi-automatic model for vulnerability exploitations of threat agents in NIST databases.

Proactive security plays a vital role in preventing the attack before entering active mode. In the modern information environment, it depends on the vulnerability management practitioners of an organization in which the critical factor is the prioritization of threats. The existing models and methodology follow the traditional approaches of a Common Vulnerability Scoring System (CVSS) to prioritize threats and vulnerabilities. The CVSS is not able to provide effectiveness to the security of the business of an organization. In contrast, the vulnerability analysis needs a model which can give significance to the prioritization policies. The model depends on the CVSS score of threats and compares various features of vulnerability like threat vectors, inputs, environments used by threat agent's groups, and potential outputs of threat agents. Therefore, the research aims to design a semi-automatic model for vulnerability analysis of threats for the National Institute of Standards and Technology (NIST) database of cyber-crime. We have developed a semi-automatic model that simulates the CVE (Common Vulnerabilities and Exposures) list of the NIST database between 1999 and 2021, concerning the resources used by the threat agents, pre-requisites input, attack vectors, and dormant results. The semi-automatic approach of the model to perform the vulnerability analysis of threat agent groups identified in a network makes the model more efficient and effective to addresses the profiling of threat agents and evaluating the CTI (Critical Threat intelligence feed). Our experimental results imply that the semi-automatic model implements the vulnerability prioritization based on the CVSS score and uses the comparative analysis based on the threat agent's vectors identified. It also provides potency and optimized complexity to an organization's business to mitigate the vulnerability identified in a network.

Model of Execution Trace Obfuscation Between Threads

Advanced reverse analysis tools have significantly improved the ability of attackers to crack software via dynamic analysis techniques, such as symbol execution and taint analysis. These techniques are widely used in malicious fields such as vulnerability exploitation or theft of intellectual property. In this paper, we present an obfuscation strategy called “execution trace obfuscation,” wherein the program execution trace repeatedly switches between multiple threads. Our technique realizes equivalent code transformation by abstracting the obfuscation problems into pruning, cloning, and coloring problems in graph theory. Based on this, we further propose the cascade encryption of a function that depends on execution trace information with a key derived from the function address calculation process, followed by removing this key from the program. We have implemented a compiler-level system that inputs a source program and automatically generates an obfuscated file. Finally, random test proves the universality of obfuscation algorithm and verify the system’s performance. Results shows that our system can effectively interfere advanced reverse analysis tools.

Tunter: Assessing Exploitability of Vulnerabilities with Taint-Guided Exploitable States Exploration

Assessing the exploitability of vulnerabilities is critical for defenders. But the vulnerability-triggering samples available for analysts often do not trigger exploitable states, making it hard to accurately assess whether the underlying vulnerabilities are exploitable. Several customized fuzzing solutions have been proposed to address this problem, by searching for new vulnerability-triggering test cases that can enter exploitable program states. However, such solutions are inefficient and in general take an overwhelmingly long time to find exploitable states, due to the large number of program paths to explore and complicated path constraints to satisfy. In this paper, we present a new automated solution Tunter to assess the exploitability of vulnerabilities. It could explore exploitable program states and generate working exploits, even if only non-exploitable vulnerability-triggering samples are given. It adopts two novel techniques: (1) a taint-guided exploration procedure to explore candidate exploitable states; and (2) a pruning mechanism to prune unwanted states for exploitation to alleviate the state explosion issue faced by symbolic execution. We have implemented a prototype of Tunter and evaluated it on 14 capture-the-flag (CTF) programs and two real-world applications. The experimental results demonstrate that it has significant performance than state-of-the-art solution Revery (Wang et al., 2018). Specifically, it finds exploitable states for these 16 programs with a 75% recall and an 88.9% precision, and eventually generates working exploits for 11 out of these 16 programs. Moreover, Tunter is 41.02 times faster than Revery.

The Association between Ageist Attitudes, Subjective Age, and Financial Exploitation Vulnerability Among Older Adults.

Ageism, or age-based negative stereotypes, prejudice, and/or discriminatory behaviors toward older adults, has been linked with various detrimental physical and psychological consequences. The current study examined the relevance of ageist attitudes to financial exploitation vulnerability (FEV) among older adults and investigated whether feeling older than one's chronological age (i.e., older subjective age) moderated the ageist attitudes-FEV association. 230 participants (M age = 72.08, SD = 5.74) filled out scales assessing ageist attitudes, subjective age, and provided relevant sociodemographic information. High levels of ageist attitudes and an older subjective age were associated with increased FEV. Moreover, the ageist attitudes-FEV association was significantly stronger among participants reporting an older subjective age. The results highlight the importance of taking into account ageism and subjective age in order to gain a deeper understanding of the underlying mechanisms which render older adults vulnerable to financial exploitation. Practical and empirical implications are discussed.

Multiomics characterization of mouse hepatoblastoma identifies yes-associated protein 1 target genes.

Hepatoblastoma (HB) is the most common primary liver malignancy in childhood and lacks targeted therapeutic options. We previously engineered, to our knowledge, the first yes-associated protein 1 (YAP1) S127A -inducible mouse model of HB, demonstrating tumor regression and redifferentiation after YAP1 withdrawal through genome-wide enhancer modulation. Probing accessibility, transcription, and YAP1 binding at regulatory elements in HB tumors may provide more insight into YAP1-driven tumorigenesis and expose exploitable vulnerabilities in HB. Using a multiomics approach, we integrated high-throughput transcriptome and chromatin profiling of our murine HB model to identify dynamic activity at candidate cis -regulatory elements (cCREs). We observed that 1301 of 305,596 cCREs exhibit "tumor-modified" (TM) accessibility in HB. We mapped 241 TM enhancers to corresponding genes using accessibility and histone H3K27Ac profiles. Anti-YAP1 cleavage under targets and tagmentation in tumors revealed 66 YAP1-bound TM cCRE/gene pairs, 31 of which decrease expression after YAP1 withdrawal. We validated the YAP1-dependent expression of a putative YAP1 target, Jun dimerization protein 2 (JDP2), in human HB cell lines using YAP1 and LATS1/2 small interfering RNA knockdown. We also confirmed YAP1-induced activity of the Jdp2 TM enhancer in vitro and discovered an analogous human enhancer in silico. Finally, we used transcription factor (TF) footprinting to identify putative YAP1 cofactors and characterize HB-specific TF activity genome wide. Our chromatin-profiling techniques define the regulatory frameworks underlying HB and identify YAP1-regulated gene/enhancer pairs. JDP2 is an extensively validated target with YAP1-dependent expression in human HB cell lines and hepatic malignancies.

The spindle assembly checkpoint is a therapeutic vulnerability of CDK4/6 inhibitor-resistant ER+ breast cancer with mitotic aberrations.

Inhibitors of cyclin-dependent kinases 4 and 6 (CDK4/6i) are standard first-line treatments for metastatic ER+ breast cancer. However, acquired resistance to CDK4/6i invariably develops, and the molecular phenotypes and exploitable vulnerabilities associated with resistance are not yet fully characterized. We developed a panel of CDK4/6i-resistant breast cancer cell lines and patient-derived organoids and demonstrate that a subset of resistant models accumulates mitotic segregation errors and micronuclei, displaying increased sensitivity to inhibitors of mitotic checkpoint regulators TTK and Aurora kinase A/B. RB1 loss, a well-recognized mechanism of CDK4/6i resistance, causes such mitotic defects and confers enhanced sensitivity to TTK inhibition. In these models, inhibition of TTK with CFI-402257 induces premature chromosome segregation, leading to excessive mitotic segregation errors, DNA damage, and cell death. These findings nominate the TTK inhibitor CFI-402257 as a therapeutic strategy for a defined subset of ER+ breast cancer patients who develop resistance to CDK4/6i.

Trimmer: An Automated System for Configuration-Based Software Debloating

Software bloat has negative implications for security, reliability, and performance. To counter bloat, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> , a static analysis-based system for pruning unused functionality. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> removes code that is unused with respect to user-provided command-line arguments and application-specific configuration files. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> uses concrete memory tracking and a custom inter-procedural constant propagation analysis that facilitates dead code elimination. Our system supports both context-sensitive and context-insensitive constant propagation. We show that context-sensitive constant propagation is important for effective software pruning in most applications. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse constant propagation</i> that performs constant propagation only for configuration-hosting variables and show that it performs better (higher code size reductions) compared to constant propagation for all program variables. Overall, our results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> reduces binary sizes for real-world programs with reasonable analysis times. Across 20 evaluated programs, we observe a mean binary size reduction of 22.7 percent and a maximum reduction of 62.7 percent. For 5 programs, we observe performance speedups ranging from 5 to 53 percent. Moreover, we show that winnowing software applications can reduce the program attack surface by removing code that contains exploitable vulnerabilities. We find that debloating using <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> removes CVEs in 4 applications.

Vulnerability prediction for secure healthcare supply chain service delivery

Healthcare organisations are constantly facing sophisticated cyberattacks due to the sensitivity and criticality of patient health care information and wide connectivity of medical devices. Such attacks can pose potential disruptions to critical services delivery. There are number of existing works that focus on using Machine Learning (ML) models for predicting vulnerability and exploitation but most of these works focused on parameterized values to predict severity and exploitability. This paper proposes a novel method that uses ontology axioms to define essential concepts related to the overall healthcare ecosystem and to ensure semantic consistency checking among such concepts. The application of ontology enables the formal specification and description of healthcare ecosystem and the key elements used in vulnerability assessment as a set of concepts. Such specification also strengthens the relationships that exist between healthcare-based and vulnerability assessment concepts, in addition to semantic definition and reasoning of the concepts. Our work also makes use of Machine Learning techniques to predict possible security vulnerabilities in health care supply chain services. The paper demonstrates the applicability of our work by using vulnerability datasets to predict the exploitation. The results show that the conceptualization of healthcare sector cybersecurity using an ontological approach provides mechanisms to better understand the correlation between the healthcare sector and the security domain, while the ML algorithms increase the accuracy of the vulnerability exploitability prediction. Our result shows that using Linear Regression, Decision Tree and Random Forest provided a reasonable result for predicting vulnerability exploitability.

(Retracted) Software development and design of network security system based on log data

With the rapid development of computer network technology and the fact that the network brings convenience to people’s lives and work, security issues cannot be ignored. Various countries and social organizations build a network security vulnerability database based on big data analysis technology, use network offensive and defensive platforms to train network security talents, and the target environment as a core component in the offensive and defensive platforms plays an important role in practice. We construct a large network security data set with a security knowledge system, analyze the current construction technology and vulnerability data characteristics of major vulnerability databases at home and abroad from multiple levels, and propose a vulnerability information correlation technology based on a combination of general vulnerability numbers and specific vulnerability numbers. Also, we comprehensively analyze each vulnerability database, assess the comprehensiveness and coverage of the vulnerability data information covered, and establish the source of the vulnerability data, data reference, security content automation protocol support, proof of concept, platform environment affected by the vulnerability exploitation code situation, and other fields of network security big data sets with security knowledge system. Here, we have completed the design and implementation of the user rights management module, the centralized monitoring module, and the service desk and configuration change management module. The system can accurately monitor the operation of the system in real time, find problems in time, warn of potential risks, and promote the operation and maintenance approach to intelligent. The transformation has improved the efficiency of fault diagnosis and greatly enhanced the safety of the system. Experimental research shows that the software system automatically screens and manually confirms the vulnerabilities that meet the target construction environment, and initially constructs a total of 43 effective target environments, which provides security researchers with the understanding of vulnerability principles, hazards, and targeted network security training. A stable and convenient experimental environment.

Division of labor within the DNA damage tolerance system reveals non-epistatic and clinically actionable targets for precision cancer medicine.

Crosslink repair depends on the Fanconi anemia pathway and translesion synthesis polymerases that replicate over unhooked crosslinks. Translesion synthesis is regulated via ubiquitination of PCNA, and independently via translesion synthesis polymerase REV1. The division of labor between PCNA-ubiquitination and REV1 in interstrand crosslink repair is unclear. Inhibition of either of these pathways has been proposed as a strategy to increase cytotoxicity of platinating agents in cancer treatment. Here, we defined the importance of PCNA-ubiquitination and REV1 for DNA in mammalian ICL repair. In mice, loss of PCNA-ubiquitination, but not REV1, resulted in germ cell defects and hypersensitivity to cisplatin. Loss of PCNA-ubiquitination, but not REV1 sensitized mammalian cancer cell lines to cisplatin. We identify polymerase Kappa as essential in tolerating DNA damage-induced lesions, in particular cisplatin lesions. Polk-deficient tumors were controlled by cisplatin treatment and it significantly delayed tumor outgrowth and increased overall survival of tumor bearing mice. Our results indicate that PCNA-ubiquitination and REV1 play distinct roles in DNA damage tolerance. Moreover, our results highlight POLK as a critical TLS polymerase in tolerating multiple genotoxic lesions, including cisplatin lesions. The relative frequent loss of Polk in cancers indicates an exploitable vulnerability for precision cancer medicine.

The Application of Machine Learning Models in Network Protocol Vulnerability Mining

With the development of society, humans are becoming more and more dependent on the Internet. And there are exploitable vulnerabilities in network sharing protocol vulnerabilities that will cause great risks to individuals and society. Therefore, vulnerability mining technology has developed into an important research problem in the field of information security. To this end, this paper uses fuzzy testing method for vulnerability mining of network protocols. The fuzzy testing technique performs vulnerability mining by sending a large amount of abnormal data to the test target and monitoring whether the software system is working properly. The vulnerability mining approach in this paper prioritizes the need to analyze and model the protocol format and generate a large number of test cases by using fuzzy test values to vary the boundaries of different parts of the protocol. These test cases are then sent to the test target, and the network state and process state of the test target are monitored in real time. Finally, if the test cases trigger a vulnerability, the system automatically records the test case information as well as the vulnerability information. The test cases evaluated by machine learning as easy to trigger vulnerabilities are sent to the test H-target, which can save the time of vulnerability mining for everyone and improve the efficiency of vulnerability mining. The vulnerability mining technology researched in this paper is of great significance to network security, which can prevent problems before they occur, discover vulnerabilities in the network in time, take effective measures to prevent them, and possibly avoid the spread of some major network vulnerability viruses.

Temozolomide-induced guanine mutations create exploitable vulnerabilities of guanine-rich DNA and RNA regions in drug-resistant gliomas.

Temozolomide (TMZ) is a chemotherapeutic agent that has been the first-line standard of care for the aggressive brain cancer glioblastoma (GBM) since 2005. Although initially beneficial, TMZ resistance is universal and second-line interventions are an unmet clinical need. Here, we took advantage of the known mechanism of action of TMZ to target guanines (G) and investigated G-rich G-quadruplex (G4) and splice site changes that occur upon TMZ resistance. We report that TMZ-resistant GBM has guanine mutations that disrupt the G-rich DNA G4s and splice sites that lead to deregulated alternative splicing. These alterations create vulnerabilities, which are selectively targeted by either the G4-stabilizing drug TMPyP4 or a novel splicing kinase inhibitor of cdc2-like kinase. Last, we show that the G4 and RNA binding protein EWSR1 aggregates in the cytoplasm in TMZ-resistant GBM cells and patient samples. Together, our findings provide insight into targetable vulnerabilities of TMZ-resistant GBM and present cytoplasmic EWSR1 as a putative biomarker.

Runtime Application Self Protection

This paper explains the fundamental concepts of Runtime Application Self-Protection Technology (RASP), a relatively new security method whose widespread use is envisaged in the near future. The ongoing focal point of this innovation is on Java and.NET stage weaknesses. In addition to typification, the paper discusses RASP's benefits and drawbacks. Despite its undeniable benefits, it is not an independent and comprehensive solution for software security. RASP provides an effective solution to avoid harmful actions when used in conjunction with tried and true traditional methods. In powerful web-based applications, script infusion weaknesses are exceptionally normal. To provide protection against distinct injection types, the necessary conditions for the production and exploitation of script injection vulnerabilities were examined. The statements were located with their types in the HTML statements using a combination of the host language and object language analysis. The information reliance connection subgraph with source and sink focuses was produced in light of the control stream diagram. For this sub-graph, a filter insertion technique is used to define multiple input data type filtering strategies. Then, using data flow analysis and intelligent filtering before important sink statements, a solution was built.

Abstract 17: Enhanced lipid metabolism in brain tumors: An exploitable metabolic vulnerability through diet-induced ketosis

Abstract Altered metabolism displayed by cancer cells helps circumvent a plethora of microenvironmental stressors. We and others have identified enhanced lipid metabolism as an important metabolic pathway in tumors of different etiologies. This “gain of function” contributes to malignant transformation in various malignancies, including brain tumors. Paradoxical to these findings, a high-fat/low carbohydrate ketogenic diet (KD) has demonstrated anti-tumor activity, both alone and in combination with radiation therapy (RT). Therefore, using our novel patient-derived GBM stem cell models, we sought to define mechanistic underpinnings contributing to this disconnect from a metabolic perspective. As an initial investigation, we fed ad libitum standard diet (SD) or KD alone or in combination with hypofractionated RT (hfRT; 6 Gy x 3) to mice injected orthotopically with aggressive GBM tumor cells. Consistent with previous reports, mice fed a KD demonstrated independent anti-tumor activity and potent synergy with RT compared to mice fed a SD. To provide a window into the metabolic consequences of KD in GBM, we performed global metabolomic profiling on tumors and serum from mice fed a SD and KD. Although ketosis was confirmed, no change in glucose was observed in serum of KD mice, suggesting carbohydrates might not be contributing to the observed anti-tumor activity. Interestingly, profound intra-tumoral metabolic changes were observed in tumors from mice fed a KD, with an accumulation of unsaturated fatty acids emerging as a central metabolic node differentiating KD and SD, which was consistent with serum studies. To begin to understand the biologic consequence of these findings, we recapitulated these findings in culture conditions in vitro. Intriguingly, culturing GBM cells with the polyunsaturated fatty acid (PUFA) linoleic acid demonstrated anti-proliferative activity, decreased clonogenic capacity, and synergy with RT in, recapitulating in vivo findings. This was not observed when cultured with the monounsaturated fatty acid oleic acid. Through a series of investigations, we went on to identify lipid storage homeostasis played a contributory role in the differential activity of these different classes of fatty acids. Specifically, we discovered that long-term storage within lipid droplets was less efficient for PUFA, leading to an increase free fatty acid accumulation in cells and lipotoxicity. The anti-tumor activity of PUFAs were rescued following inhibition of lipolysis. These studies collectively reaffirm and provide mechanistic underpinnings of the antitumor activity of KD, suggesting enhanced FA metabolism in brain tumors may serve as an exploitable metabolic vulnerability through diet modification. In addition, it provides a framework to potentially modify this diet to both enhance its antitumor activity and improve tolerability to allow for clinical implementation. Citation Format: Shiva Kant, Pravin Kesarwani, Yi Zhao, Prakash Chinnaiyan. Enhanced lipid metabolism in brain tumors: An exploitable metabolic vulnerability through diet-induced ketosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 17.

The Role of Normative Age-Graded Transitions and Human Agency in Patterns and Variations of Financial Exploitation of Older Adults

Objectives: Drawing from normative age-graded transitions and human agency, this study provides a theoretical and empirical exploration of the patterns and variations of financial exploitation of older adults. Methods: The study employs qualitative methods with data collected from focus groups and interviews with residents of a large retirement community. Results: Normative age-graded transitions—such as retirement, residency relocations, changes in social support networks, medical events, death or incapacitation of a spouse, grief, declines in brain and physical health and cognition—lead to assessments of older adults’ sense of self-efficacy regarding their abilities to deal with everyday tasks, challenges, and decisions. If these assessments result in lowered self-efficacy, that reaches a threshold, cognitive transformations can occur, producing an increased vulnerability for financial exploitation. For those whose assessments of self-efficacy remained stable, financial exploitation was avoided. Conclusions: The role of normative age-graded transitions and general assessments of self-efficacy, thresholds, and cognitive transformations provides a promising theoretical approach for explaining patterns and variations of financial exploitation of older adults. These findings, if confirmed with more representative samples, can help validate the role of normative age-graded transitions and human agency in explaining why some, but not other, older adults fall victim to financial exploitation.

Abstract 3725: SETD2 loss in renal carcinoma cells induces the unfolded protein response

Abstract Introduction: SETD2 encodes a histone H3-K36 methyltransferase which is frequently inactivated in clear cell renal carcinomas (ccRCCs) and papillary RCCs via 3p deletion/LOH and deleterious mutations. Histone H3-K36 trimethylation is facilitated by SETD2, which is necessary for proper pre-mRNA intron splicing. Improperly spliced mature mRNA may lead to aberrant translation of retained introns (ATaRI), which represents potential therapeutic vulnerabilities. We explored this hypothesis using real world data and RCC models. Methods and Results: Gene set enrichment analysis comparing SETD2-mutant to WT tumors using samples from the TCGA KIRC data set revealed that the unfolded protein response (UPR) was strongly enriched, as well as several immunotherapy-relevant pathways. This suggested that peptides arising from ATaRI may be present, since they would not be expected to fold properly and thus need to be addressed by the UPR pathway to maintain homeostasis. To investigate this further, we generated Setd2-isogenic RENCA cells using CRISPR. Knockout was confirmed by sequencing and immunoblot. H3K36 trimethylation was decreased or eliminated in monoclonal knockout cell lines, confirming a functional effect. Markers of UPR activation, including Atf4 and cleaved Atf6, were found to be upregulated in Setd2-mutant RENCA cells compared to controls as measured by immunoblot. Cleaved ATF6 translocates to the nucleus to induce the UPR transcriptional program. Consistent with this, Atf6 was found to localize to the nucleus in Setd2-knockout cells using immunofluorescence (IF). Analysis of tissue microarrays of human SETD2-mutant vs -WT ccRCC revealed increased ATF6 signal in areas with low H3K36 tri-methylation, indicating UPR activation in vivo. Interestingly, CHOP, another downstream effector of the UPR pathway which predominantly regulates cell death, did not become upregulated in Setd2-knockout cells, suggesting activation of a compensatory cell survival pathway. Geldanamycin was shown to destabilize the Perk and Ire1a arms of the UPR, which resulted in increased cell death in Setd2 mutant cells using Cell Titre glo. Additionally, H3K36 trimethylation has been implicated in directing homologous repair of DNA, and blunted responses to agents that induce DNA double-strand breaks of p-Atm, p53, and Rad51 were observed by immunoblot and IF. The inability to detect DNA damage in Setd2 mutant cells however did not confer sensitivity to PARP inhibitors. Conclusions: We identify activation of the UPR upon Setd2 loss and suggest that activation of part of the UPR pathway may represent a new therapeutic vulnerability for exploitation as a rationale for personalized medicine. We further characterize the altered DNA damage response in the setting of Setd2 loss. We continue to evaluate the generation of peptides arising from ATaRI in Setd2-mutant contexts. Citation Format: Alexander Metz, Marya Kozinova, Robert Uzzo, Jessica Peskin, Michael Slifker, Janusz Franco-Barraza, Edna Cukierman, Philip Abbosh. SETD2 loss in renal carcinoma cells induces the unfolded protein response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3725.